Browsing information from large datasets can be a challenging exercise that becomes difficult when multiple datasets and/or changing datasets are involved. There is often a need to keep track of multiple datasets of information containing dynamic content. Such dynamic content may for example include data-points whose attributes change constantly in response to inputs from other users, or data-points which are being newly created. It becomes increasingly difficult for users to manually keep track of such large information spaces, whether dynamic or static.
FIGS. 1A and 1B show two known visualizations for providing users with greater awareness of information, using the concept of peripheral awareness. The visualization 100 of FIG. 1A utilizes a side bar 102 placed on a user's workspace. The side bar 102 displays information that is updated automatically. The visualization 104 of FIG. 1B uses a combination of a list view and a tree view to summarize information related to all data points in a user's workspace. The list and tree view provides a linearization of a multidimensional dataset. Data is aggregated under heading sub-views, which can be minimized and maximized.
Other visualizations 200, 202, 204, 206, such as those shown by FIGS. 2A-2D, offer either one or more of the following visual functionalities: an overview of the entire workspace, a peripheral view of the workspace, re-aggregation of data in the visualization, multiple regions of interest, an ability to compare between data points based on certain attributes, and aggregate versus focused vision in the same view.
The publication by Jing Yang et al., “Interactive hierarchical displays: a general framework for visualization and exploration of large multivariate datasets”, Computer & Graphics, V27, N2, April 2003, pages 265-283, describes a framework for visualizing large multivariate datasets. The underlying principle of this framework is to develop a multi-resolution view of the data via hierarchical clustering, and to use hierarchical variations of traditional multivariate visualization techniques to convey aggregation information about the resulting clusters. Users can then explore their desired focus region at different levels of detail, using our suite of navigation and filtering tools
The publication by Gansner et al., “Topological fish eye view for visualizing large graphs” (http://www.research att.com/areas/visualization/papers_videos/papers/2004gkn—1.pdf) describes visualization to effectively layout dense graphs. Gansner et al. propose a topological zooming method. Which pre-computes a hierarchy of coarsened graphs that are combined on-the-fly into renderings, with the level of detail dependent on distance from one or more foci. A related geometric distortion method yields constant information density displays from these renderings
There is a need, however, to allow users to keep track of relevant information even when their attention is not focused on the data-space of the relevant information (i.e. peripheral view of whole workspace), and/or allow users to maintain an awareness of the entire data-space though the users may be focusing with greater attention on a particular set of data-points.
There is further a need for providing visualization of information in a manner so as to allow comparison of the information based on attributes and values of such information, or of functions thereof.